Editing IPCC:AR6/WGI/Chapter-5 (section)

=== 5.4.11 Near-term Prediction of Ocean and Land Carbon Sinks ===

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The AR5 (WGI, [[IPCC:Wg1:Chapter:Chapter-11#11.3.2|Section 11.3.2]] ) assessed near-term climate predictability based on ESMs initialized from the observed climate state. Since AR5, a growing number of prediction systems have been developed based on ESMs that include the ocean and land carbon cycle components. Predictability of key physical climate variables (assessed in Chapter 4) provides a platform to establish predictive skill for interannual variations in the strength of the natural carbon sinks in response to internal climate variability. In most systems the carbon cycle components are only indirectly initialized and respond to the initialized climate variations ( [[#Li--2019|Li et al., 2019]] ). This subsection synthesizes information on predictability of the land and ocean carbon sinks using both the idealized potential predictability and the actual predictability skill measures.

Longer-term memory residing in the ocean enables predictability of the ocean carbon sink ( [[#McKinley--2017|McKinley et al., 2017]] ; [[#Li--2018|Li and Ilyina, 2018]] ). The predictive horizon of the globally integrated air–sea CO <sub>2</sub> fluxes has been assessed in perfect-model frameworks that are based on an idealized ensemble of simulations in which each ensemble member serves as a verification, while no observations are assessed. Perfect-model studies provide an estimate of the upper range of potential predictability for the integrated air–sea CO <sub>2</sub> fluxes of about two years globally and up to a decade in some regions ( [[#Séférian--2018a|Séférian et al., 2018a]] ; [[#Spring--2020|Spring and Ilyina, 2020]] ). Evidence is also emerging for predictive skill of the global air–sea CO <sub>2</sub> fluxes of up to six years based on prediction systems initialized with observed physical climate states ( [[#Ilyina--2021|Ilyina et al., 2021]] ), with a potential for even longer-term regional predictability in some regions, including the North Atlantic and subpolar Southern Ocean (H. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Lovenduski--2019a|Lovenduski et al., 2019a]] ).

Models suggest that predictability of the air–sea CO <sub>2</sub> flux is related to predictability of ocean biogeochemical state variables such as dissolved inorganic carbon (DIC) and total alkalinity (TA; [[#Lovenduski--2019a|Lovenduski et al., 2019a]] ), as well as the mixed layer depth (H. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ). Temperature variations largely control shorter-term predictability of the ocean carbon sink, while longer-term predictability is related to non-thermal drivers such as ocean circulation and biology ( [[#Li--2019|Li et al., 2019]] ). Although there is a substantial spatial heterogeneity, initialized predictions suggest stronger multi-year variations of the air–sea CO <sub>2</sub> flux and generally tend to outperform uninitialized simulations on the global scale ( [[#Li--2019|Li et al., 2019]] ). The predictive skill of air–sea CO <sub>2</sub> flux shows a consistent spatial pattern in different models, despite the wide range of techniques used to assimilate observational information ( [[#Regnier--2013|Regnier et al., 2013]] ). ESM-based prediction systems also demonstrate predictability of other marine biogeochemical properties such as net primary production ( [[#Séférian--2014|Séférian et al., 2014]] ; [[#Yeager--2018|Yeager et al., 2018]] ; [[#Park--2019|Park et al., 2019]] ) and seawater pH ( [[#Brady--2020|Brady et al., 2020]] ).

Seasonal predictability of air-land CO <sub>2</sub> flux up to 6–8 months is driven by the state of El Niño–Southern Oscillation (ENSO) ( [[#Zeng--2008|Zeng et al., 2008]] ; [[#Betts--2018|Betts et al., 2018]] ). Fewer land carbon initialized predictions are available from decadal prediction systems, yet they tend to outperform the uninitialized simulations in capturing the major year-to-year variations, as indicated by higher correlations with the global carbon budget estimates. There is growing evidence that the potential predictive skill of air-land CO <sub>2</sub> flux is maintained out to a lead-time of two years ( [[#Lovenduski--2019b|Lovenduski et al., 2019b]] ); this predictability horizon is also supported by perfect model studies ( [[#Séférian--2018a|Séférian et al., 2018a]] ; [[#Spring--2020|Spring and Ilyina, 2020]] ). The origins of this interannual predictability are not yet fully understood. However, they seem to be associated with the oscillatory behaviour of ENSO ( [[#Séférian--2014|Séférian et al., 2014]] ) and the drivers of terrestrial carbon flux predictability, such as ecosystem respiration and gross primary production ( [[#Lovenduski--2019a|Lovenduski et al., 2019a]] ). Initialized simulations suggest that observed variability in the land carbon sink is improved through initialization of prediction systems with the observed state of the physical climate.

The predictability horizon of variations in atmospheric CO <sub>2</sub> growth rate is not yet fully established in the literature. However, predictive skill of the land and ocean carbon sinks show a potential to establish predictability of variations in atmospheric CO <sub>2</sub> up to two years in advance in the initialized prediction systems, with an upper bound of up to three years in a perfect-model study ( [[#Spring--2020|Spring and Ilyina, 2020]] ); this skill is primarily limited by the terrestrial carbon sink predictability.

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